There are an estimated two million people with epilepsy in the United States. Computerized analysis of electroencephalogram (EEG) data has been the goal of many investigators for years. Such an analysis system could be used for diagnosing seizures, for assessing the effectiveness of medical therapy or for selecting patients for epilepsy surgery.
A necessary component of accurate assessment of medical therapy, including safe and effective surgery, requires prolonged EEG monitoring to obtain recordings of seizure activity. One important reason for developing a system is to meet the needs of about one-third of the many patients with complex partial seizures who do not respond satisfactorily to anti-epileptic drug therapy.
Three devices can be developed to assist in the treatment of epilepsy. The first is a microcomputer-based system designed to process massive amounts of electroencephalogram (EEG) data collected during long-term monitoring of patients for the purpose of diagnosing seizures, assessing the effectiveness of medical therapy or selecting patients for epilepsy surgery. Such a device would select and display important EEG events. Currently many such events are missed. A second device would be a portable version of the first. This device would allow the patient to be monitored while away from the hospital. A third device could be implanted and would detect seizures and initiate therapy.
Computerized systems can be used to collect and store the data, but even with new mass-storage devices, it is not practical to store and review all of the data collected over days. Automatic spike and sharp wave detection and automatic seizure detection algorithms could be used to detect critical events and store just the relevant data for later examination by the neurologist and neurosurgeons. This information, in conjunction with clinical observation, is used to localize epileptic form discharges. Reliable automatic seizure detection is perhaps more challenging and would be a key element of any system designed for analysis of EEG data.
Five groups of investigators have pursued the goal of automatic seizure detection by implementing a system and publishing their results. Prior, et. al. (Prior, P. F., Maynard, D. E., "Recording Epileptic Seizures," Proceedings of the Seventh International Symposium of Epilepsy, Berlin (West), Thieme Edition, Publishing Sciences Group, Inc., 325-377 (1975).) developed a simple system aimed at recording the timing and frequency of seizure discharges. They were able to detect severe seizures by looking for large amplitude signals sustained over a period of time. Their system used a single channel of data recorded on paper running at 5 to 6 cm/hour.
Babb, et al. (Babb, T. L., Mariani, E., Crandall, P. H., "An Electronic Circuit for Detection of EEG Seizures Recorded with Implanted Electrodes,"Electroencephalograph and Clinical Neurophysiology, 37, 305-308 (1974).) developed electronic circuits for recording and detecting seizures. Their system used implanted electrodes to detect seizures which might otherwise go unreported. The system identified as seizure high-amplitude EEG signals of sufficient duration. They reported monitoring four patients and detecting 66 seizures, 20 of which were false detections. During this same period of time, the nurses detected 28 seizures, two of which were false detections. Therefore, the automated system detected 20 unreported seizures.
Ives, et. al. (Ives, J. R., Thompson, C. J., Gloor, P., "Seizure Monitoring: A New Tool in Electroencephalography," Electroencephalography and Clinical Neurophysiology, 41, 422-427 (1976).) used implanted depth electrodes and a PDP-12 computer to remotely monitor patients. The system was adjusted or calibrated for individual patients using their seizure patterns. If the EEG signals fell within an amplitude window for a certain period of time, the algorithm identified the event as a seizure and recorded eight channels of EEG data. During two weeks of monitoring, thirteen seizures were detected and recorded by the computer. Only one seizure was reported by the nursing staff and only 3 were noted by the patient.
If Gotman (Gotman, J., "Automatic Seizure Detection: Improvements and Evaluations," Electroencephalograph and Clinical Neurophysiology, 76, 317-324 (1990).) designed a system to select, from largely uneventful EEG data, the sections which are likely to be of interest. Events, such as seizures, are recorded either because the program detects them or because the event button is pressed. His system was originally implemented in 1975 and has been in use since that time. It detects seizures using both surface and depth electrodes. His system is in use in many centers and has been updated. A study of his latest algorithm used 5303 hours of data and showed that 24 percent of the 244 seizures recorded were missed by the automatic detection system. However, in 41 percent of the seizures, the patient alarm was not pressed, but the computer made the decision to record the event. Like the other systems, Gotman's system detects many seizures which would otherwise be missed. The system experiences about one false detection per hour of recording.
Gotman's method uses digitally filtered data broken up into 2-second epochs. He compares features of this data to what he calls "background." The background data is a 16-second long section of data ending 12 seconds prior to the epoch being analyzed. This comparison allows the algorithm to self-scale to account for differences in montage or other settings and is a form of what we will refer to as calibration or standardization. Detection occurs when 1) the average amplitude of half-waves in the epoch is at least three times that of the background, 2) their average duration corresponds to frequencies between 3 and 20 Hz, and 3) the coefficient of variation (ratio of the variance to the square of the mean) of the half-waves is below 0.36. In short, the algorithm is based on amplitude, frequency, and the regularity of the half-wave duration. In 1990, Gotman reported modifications to his algorithm including the requirement to look 8 seconds ahead to verify that the amplitude remains high. This modification reduced false detections.
More recently Murro et al., (Murro, A. M., King, D. W., Smith, J. R., Gallagher, B. B., Flanigin, H. F., Meador, K., "Computerized Seizure Detection of Complex Partial Seizures," Electroencephalography and Clinical Neurophysiology, 79, 330-333 (1991).) developed a system using concepts similar to those of Gotman but used spectral concepts for feature development. They used three features from each of two channels. These six features are reduced to four using principal component analysis, and then statistical discrimination is used to develop a detection rule. Their algorithm was developed for intracranial data only.
Murro, et. al. address the issue of calibrating for patient differences partly in the way they define their features. They define reference power as the spectral power between 0.15 and 36 Hz averaged over four consecutive EEG epochs from 27 to 55.6 seconds prior to the event being evaluated. One of their features is relative power and is defined as the power between 0.15 and 36 Hz of the current epoch divided by the reference power. The second of their features is the dominant frequency. The third feature is rhythmicity, which is the ratio of the power associated with the dominant frequency to the relative power. Murro, et. al. use a segment of recent EEG data from the patient to influence the definition of their first and third feature. However, Murro, et al. also provide more in the way of custom tailoring their algorithm to the individual by building a separate decision rule for each patient. Their rule is based on normal data from the patient collected at different times and seizure data collected from other patients. These procedures allow for a more careful calibration for patient differences.
The algorithm of Murro, et. al. was tested using 8 patients and 43 seizures. Their system was evaluated using different detection thresholds. It detected all seizures allowing for a rate of 2.5 false detections per hour and detected 91% of the seizures with 1.5 false detections per hour.
The first seizure detection methods relied on amplitude and duration to identify seizures. Gotman's method tends to mimic the EEG readers. He characterizes the signals using features appearing to be motivated by those observed during seizure activity. His self-scaling and his detection rules are simple but effective.
The methods of Murro, et al. are similar to Gotman's but are more statistically sophisticated. Like Gotman, Murro, et. al., used recent epochs to self-scale their features. They characterize the epochs of data using relative amplitude, dominant frequency and rhythmicity and then use statistical discriminate analysis to develop a detection rule.
There are three important common elements in these two algorithms and in the algorithm of the invention:
1) standardization, calibration or self-scaling
2) feature selection
3) discrimination or a decision rule
The most critical element in discriminating seizure from other data is the definition and evaluation of features or feature selection. However, new standardization or calibration methods are also essential.